<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>Thinknook &#187; Data-Mining</title>
	<atom:link href="http://thinknook.com/category/sql-server/data-mining/feed/" rel="self" type="application/rss+xml" />
	<link>http://thinknook.com</link>
	<description>Because the world needs another Business Intelligence blog!</description>
	<lastBuildDate>Tue, 03 Jun 2014 22:09:23 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>hourly</sy:updatePeriod>
	<sy:updateFrequency>1</sy:updateFrequency>
	<generator>https://wordpress.org/?v=4.1.41</generator>
	<item>
		<title>10 Tips to Improve your Text Classification Algorithm Accuracy and Performance</title>
		<link>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=10-ways-to-improve-your-classification-algorithm-performance</link>
		<comments>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/#comments</comments>
		<pubDate>Mon, 21 Jan 2013 12:06:35 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[bigrams]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[corpus]]></category>
		<category><![CDATA[predictiion]]></category>
		<category><![CDATA[stopwords]]></category>
		<category><![CDATA[text classification]]></category>
		<category><![CDATA[unigrams]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=934</guid>
		<description><![CDATA[In this article I discuss some methods you could adopt to improve the accuracy of your text classifier, I&#8217;ve taken a generalized approach so the recommendations here should really apply for most text classification problem you are dealing with, be it Sentiment Analysis, Topic Classification or any text based classifier. This is by no means [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/feed/</wfw:commentRss>
		<slash:comments>17</slash:comments>
		</item>
		<item>
		<title>Text Classification Threshold Performance Graph</title>
		<link>http://thinknook.com/text-classification-threshold-performance-graph-2013-01-20/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=text-classification-threshold-performance-graph</link>
		<comments>http://thinknook.com/text-classification-threshold-performance-graph-2013-01-20/#comments</comments>
		<pubDate>Sun, 20 Jan 2013 20:08:23 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=927</guid>
		<description><![CDATA[One way to increase the accuracy of a classification algorithm is to allow the algorithm to return an &#8220;Unknown&#8221; value, particularly when the probability of what we are trying to classify is too low to simply belong in one class and the algorithm is essentially guessing an answer, leading to incorrect classification. In this post [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/text-classification-threshold-performance-graph-2013-01-20/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>Testing &amp; Diagnosing a Text Classification Algorithm</title>
		<link>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=testing-diagnosing-a-text-classification-algorithm</link>
		<comments>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/#comments</comments>
		<pubDate>Sat, 19 Jan 2013 17:37:26 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Classification]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[accuracy]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[confusion matrix]]></category>
		<category><![CDATA[nltk]]></category>
		<category><![CDATA[precision]]></category>
		<category><![CDATA[recall]]></category>
		<category><![CDATA[text classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=922</guid>
		<description><![CDATA[To get something going with text (or any) classification algorithm is easy enough, all you need is an algorithm, such as Maximum Entropy or Naive Bayes, an implementation of each is available in many different flavors across various programming languages (I use NLTK on Python for text classification), and a bunch of already classified corpus data [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/testing-diagnosing-a-text-classification-algorithm-2013-01-19/feed/</wfw:commentRss>
		<slash:comments>2</slash:comments>
		</item>
		<item>
		<title>Generic Trend Classification Engine using Pearson Correlation Coefficient</title>
		<link>http://thinknook.com/approaching-trend-analysis-through-discretization-and-correlation-2012-12-16/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=approaching-trend-analysis-through-discretization-and-correlation</link>
		<comments>http://thinknook.com/approaching-trend-analysis-through-discretization-and-correlation-2012-12-16/#comments</comments>
		<pubDate>Sun, 16 Dec 2012 22:45:38 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[Pearson correlation]]></category>
		<category><![CDATA[social trends]]></category>
		<category><![CDATA[trend analysis]]></category>
		<category><![CDATA[trend classification]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=886</guid>
		<description><![CDATA[Trend analysis in my experience is generally done through manual (human) review and exploration of data through various BI tools, these tools do a great job by visually highlighting data that can be of interest to the data analyst, and when coupled with data-mining techniques such as clustering and forecasting, it gives us invaluable and [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/approaching-trend-analysis-through-discretization-and-correlation-2012-12-16/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>NLTK Megam (Maximum Entropy) Library on 64-bit Linux</title>
		<link>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=nltk-megam-maximum-entropy-library-on-64-bit-linux</link>
		<comments>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/#comments</comments>
		<pubDate>Tue, 27 Nov 2012 15:57:46 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Coding]]></category>
		<category><![CDATA[Coding Libraries]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[Sentiment Analysis]]></category>
		<category><![CDATA[classification]]></category>
		<category><![CDATA[logistic regression]]></category>
		<category><![CDATA[max ent]]></category>
		<category><![CDATA[megam]]></category>
		<category><![CDATA[natural language processing]]></category>
		<category><![CDATA[nltk]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=837</guid>
		<description><![CDATA[NLTK (Natural Language Toolkit) is a Python library that allows developers and researchers to extract information and annotations from text, and run classification algorithms such as the Naive Bayes or Maximum Entropy, as well as many other interesting Natural Language tools and processing techniques. The Maximum Entropy algorithm from NLTK comes in different flavours, this post will [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/nltk-megam-maximum-entropy-library-on-64-bit-linux-2012-11-27/feed/</wfw:commentRss>
		<slash:comments>12</slash:comments>
		</item>
		<item>
		<title>Twitter Sentiment Analysis Training Corpus (Dataset)</title>
		<link>http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=twitter-sentiment-analysis-training-corpus-dataset</link>
		<comments>http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/#comments</comments>
		<pubDate>Sat, 22 Sep 2012 16:13:49 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Sentiment Analysis]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=710</guid>
		<description><![CDATA[An essential part of creating a Sentiment Analysis algorithm (or any Data Mining algorithm for that matter) is to have a comprehensive dataset or corpus to learn from, as well as a test dataset to ensure that the accuracy of your algorithm meets the standards you expect. This will also allow you to tweak your [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/twitter-sentiment-analysis-training-corpus-dataset-2012-09-22/feed/</wfw:commentRss>
		<slash:comments>36</slash:comments>
		</item>
		<item>
		<title>What is Business Intelligence (BI)</title>
		<link>http://thinknook.com/what-is-business-intelligence-bi-2012-07-26/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=what-is-business-intelligence-bi</link>
		<comments>http://thinknook.com/what-is-business-intelligence-bi-2012-07-26/#comments</comments>
		<pubDate>Thu, 26 Jul 2012 17:30:54 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Business Intelligence]]></category>
		<category><![CDATA[Data-Mining]]></category>
		<category><![CDATA[MS SQL Server]]></category>
		<category><![CDATA[Power View]]></category>
		<category><![CDATA[SSAS]]></category>
		<category><![CDATA[SSRS]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=642</guid>
		<description><![CDATA[Ok, this might be abit of a general question, as am sure anyone who found themselves in this blog knows a thing or two about BI, but in this post I will try to give a more holistic overview of what is a full Business Intelligence offering, and what dimensions constitutes a full analytical offering. [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/what-is-business-intelligence-bi-2012-07-26/feed/</wfw:commentRss>
		<slash:comments>1</slash:comments>
		</item>
		<item>
		<title>SSAS Session Mining Object Creation Exception</title>
		<link>http://thinknook.com/ssas-session-mining-object-creation-exception-2012-03-08/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=ssas-session-mining-object-creation-exception</link>
		<comments>http://thinknook.com/ssas-session-mining-object-creation-exception-2012-03-08/#comments</comments>
		<pubDate>Thu, 08 Mar 2012 12:05:59 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Data-Mining]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=472</guid>
		<description><![CDATA[The Data-Mining Excel Plugin for SQL Server 2008 is one of the more awesome tools in the Microsoft BI tool-set, although might require some configuration before deployment into the business. While I was trying to roll-out this solution (to a test group), I ran into the following error message: Error (data mining) Session Mining objects [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/ssas-session-mining-object-creation-exception-2012-03-08/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
		<item>
		<title>SQL Server Data-Mining EXCEL Plug-in Demo Video</title>
		<link>http://thinknook.com/sql-server-data-mining-excel-plug-in-demo-video-2012-03-08/?utm_source=rss&#038;utm_medium=rss&#038;utm_campaign=sql-server-data-mining-excel-plug-in-demo-video</link>
		<comments>http://thinknook.com/sql-server-data-mining-excel-plug-in-demo-video-2012-03-08/#comments</comments>
		<pubDate>Wed, 07 Mar 2012 23:25:07 +0000</pubDate>
		<dc:creator><![CDATA[Links Naji]]></dc:creator>
				<category><![CDATA[Data-Mining]]></category>

		<guid isPermaLink="false">http://thinknook.com/?p=458</guid>
		<description><![CDATA[The Data-Mining Excel Plugin from SQL Server is one of the most powerful tools available to Data Analysts and Power-Users, by leveraging SQL Server&#8217;s SSAS (Analysis Service), the Data-Mining Plugin is able to make cutting edge Data-Mining Algorithms very accessible and easy to use, and with extended features such as creating, customizing and training your [&#8230;]]]></description>
		<wfw:commentRss>http://thinknook.com/sql-server-data-mining-excel-plug-in-demo-video-2012-03-08/feed/</wfw:commentRss>
		<slash:comments>0</slash:comments>
		</item>
	</channel>
</rss>

<!--
Performance optimized by W3 Total Cache. Learn more: https://www.w3-edge.com/products/


Served from: thinknook.com @ 2026-05-30 20:41:14 by W3 Total Cache
-->